Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores probability convergence, discussing conditions for random variable sequences to converge and the uniqueness of convergence.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
